1. Field of the Invention
The present invention relates to an output parameter estimation apparatus that uses a neural network, and more specifically relates to an output parameter estimation apparatus that is capable of learning according to various ranges of input parameters.
2. Description of the Prior Art
It is sometimes difficult to analyzes the input/output cause-effect relation in physical or chemical systems. However, outputs can be estimated from inputs based on learning functions of neural networks. Hence the neural networks have increasingly been used in control apparatuses for controlling complex systems, and especially for controlling targets that are extremely nonlinear. A representative of such apparatuses is air/fuel ratio control apparatuses for internal combustion engines in automobiles.
Many conventional cars have been fitted with catalytic converters to reduce the amount of pollutants, such as NOx, CO, and HC, present in exhaust fumes, with three-way catalytic converters being a representative example of such. Here, the air/fuel ratio must be kept at a fixed value that enables the most effective conversion of the pollutants by the three-way catalyst. For the above purpose, air/fuel ratio control that can keep the ratio at the fixed value with high accuracy regardless of the operational state of the engine is necessary.
Usually, such air/fuel ratio control performs feed-forward control when the driver adjusts the throttle to make compensated increases and decreases in the amount of injected fuel. It is also common for air/fuel ratio control to perform feedback control of a compensatory amount of injected fuel using readings given by an air/fuel ratio sensor. These control operations are especially effective under normal driving conditions, such as when the engine is idling or being driven at a constant speed. However, when the engine condition is in a transitional state, which here refers to acceleration or deceleration, the air/fuel ratio cannot be controlled accurately, since the detection delay of the air/fuel ratio sensor and the amount of injected fuel actually flowing into cylinders vary in a complex way with the driving conditions and the external environment. Thus, in reality it has been very difficult to accurately control the air/fuel ratio using only simple feed-forward and feedback control.
In order to improve the precision of such control, Japanese Laid-Open Patent Application 8-74636 discloses the use of a neural network to learn the nonlinear aspects, such as fuel coating caused by some of the injected fuel not flowing into the cylinders but sticking to the walls of the air intake pipe, and to calculate a fuel amount to be injected in order to increase the responsiveness of the control apparatus to changes in the operational state of the engine.
FIG. 25 shows an example of an air/fuel ratio control apparatus that uses a conventional neural network.
In this air/fuel ratio control apparatus, a state detection unit 210 detects physical values which shows the state of an engine E, such as the engine RPM (Ne), the intake air pressure (Pb), the present throttle amount (THL), the injected fuel amount (Gf), the intake air temperature (Ta), the cooling water temperature (Tw), and the air/fuel ratio (A/Fk) itself. These detected physical values are inputted into a neural network (NN) operation unit 220 which uses a neural network to estimate the actual air/fuel ratio (A/Fr) that cannot be accurately detected by the air/fuel ratio sensor when the engine condition is in a transitional state. An injected fuel amount calculation unit 230 then performs feedback control to minimize a deviation between the estimated air/fuel ratio (A/FNN) and the ideal air/fuel ratio (A/Fref) and calculates an injected fuel amount (Gb) that realizes the ideal air/fuel ratio Thus, precise air/fuel ratio control becomes possible by estimating the actual air/fuel ratio (A/Fr) for complex transitional engine conditions.
FIG. 26 shows the construction of the neural network used in the NN operation unit 220. This neural network is composed of three layers. Physical values of the engine RPM (Ne), the intake air pressure (Pb), the present throttle amount (THL), the injected fuel amount (Gf), the intake air temperature (Ta), the cooling water temperature (Tw), and the air/fuel ratio (A/Fk) detected by the state detection unit 210 are inputted in the first layer as input parameters. Here, though the air/fuel ratio (A/Fk) is the most recent air/fuel ratio detected by the air/fuel ratio sensor in control cycles, this ratio does not show the actual air/fuel ratio (A/Fr) that represents a combustion result of the current injected fuel, due to the time-delay of the air/fuel ratio sensor. The input parameters are each multiplied by each one of a plurality of weights, and the total of multiplication results corresponding to the input parameters is added to a threshold value and then converted according to a transfer function in each unit in the second layer. Here, "unit" means a basic component of the neural network that is set in each layer of the neural network. Each converted value is further multiplied by a corresponding weight and the multiplication results are totaled in the unit in the third layer. Another threshold value is added to the total and the addition result is converted according to another transfer function. As a result, the estimated air/fuel ratio (A/FNN) is obtained.
The learning process for the neural network used by the NN operation unit 220 is shown in FIG. 27. The figure shows a model example of the learning process for the neural network which obtains the estimated air/fuel ratio (A/FNN) with the engine RPM (Ne), the intake air pressure (Pb), the present throttle amount (THL), the injected fuel amount (Gf), the intake air temperature (Ta), the cooling water temperature (Tw), and the air/fuel ratio (A/Fk) as its input parameters (hereinafter, this set of input parameters is simply referred to as "input data set").
Here, a state detection unit 210 same as that shown in FIG. 25 is installed in the engine E to gather input data sets when driving a real car. The gathered input data sets are each converted to a learning data set by a learning data generation unit a in view of the detection delay of the air/fuel ratio sensor and the changes in operational characteristics of the engine. This learning data set is made up of an input data set and teaching data of air/fuel ratio (A/Ft). Here, the teaching data (air/fuel ratio (A/Ft)) matches the actual air/fuel ratio (A/Fr) and is generated from the detected air/fuel ratio (A/Fk) in view of the detection delay of the air/fuel sensor. While it is preferable for previously obtained input data sets to also be included in the learning data sets and for the learning data sets to be used as time series data, the previous input data sets are not used in the present example for simplifying the explanation. The learning data sets are then stored in a learning data storage unit b and used by a learning execution unit c for the learning of the neural network. Here, each input data set is inputted in the neural network which detects the deviation e between the estimated air/fuel ratio (A/FNN) and the corresponding teaching data (A/Ft) as its output. The neural network then progressively changes its connection condition, including weights and threshold values, according to a back propagation method so that this deviation e falls within a permitted range, such as 0.1 on the air/fuel ratio scale.
This learning process for the neural network must be performed with high accuracy in order to improve the control accuracy of the conventional air/fuel ratio control apparatuses such as above.
For this aim, learning data sets are prepared so as to cover not just one specific state of the engine but various operational states of the engine. However, the learning data sets generated as such may still not be sufficient for accurately estimating the actual air/fuel ratio. Accordingly, it is necessary to repeat a development routine composed of judging the learning result, generating new learning data sets by gathering input data sets corresponding to a field where the learning result is judged as poor, and relearning using the new learning data sets. "Field" here refers to the operational state of the engine that is specified by a combination of ranges of at least two input parameters.
When the deviation e for one field is outside the permitted range, the amount of learning data sets for that field is increased and relearning is performed to reduce the deviation e. In such a case, the new learning data sets for the field have to be selected so as not to disturb the balance of the amount of learning data sets for each field. However, when the neural network executes the relearning using the newly generated learning data, there is a possibility that a field where the learning result is judged as satisfactory in the previous stage was judged as unsatisfactory in this relearning stage, or even the learning results of the neural network as a whole is degraded. When this happens, the selection of input data sets, the generation of new learning data sets, and the relearning must be repeated until the learning result for each field is judged as satisfactory. This requires a considerable amount of time. Thus, it is very difficult to improve the estimation accuracy of the air/fuel ratio of the neural network for various fields other than by depending on trial-and-error methods
Also, it is impossible to cover all possible operational states of the engine in the learning process for the neural network at the time of production. Accordingly, the learning is performed by the above trial and error methods for predictable operational conditions so that the air/fuel ratio can be accurately controlled at least under those predictable conditions. However, when an operational condition which is not covered by the learning occurs, it is difficult to control the air/fuel ratio with high accuracy since the estimation accuracy of the neural network may decrease. A failsafe function is usually used for stopping the control when parameters which are beyond the range covered by the learning are inputted.
In the air/fuel ratio control apparatus described above, the learning range of the neural network corresponds to predictable driving states. When a driving state is outside the learning range, such as when driving the car in the mountains or when performance of the air/fuel ratio sensor deteriorates, it is difficult to keep the air/fuel ratio at the desired ratio.
Online learning adaptive neural networks have been suggested as a solution of the above problem. However, such conventional neural networks are only effective when repeating fixed operations but not always effective in air/fuel ratio control when performing various operations at random, since the estimation accuracy for a field that was judged as satisfactory in the previous learning stage may deteriorate in the relearning stage.